Systemic Immun e–Inflammation Index as a Predictor for Head and Neck Cancer Prognosis: A Meta-Analysis
Bibliographic record
Abstract
Background Studies have reported inconsistent results regarding the prognostic value of the systemic immune–inflammation index (SII) in head and neck cancer (HNC). Thus, the present meta-analysis assessed the literature on the prognostic value of SII in those with HNC. Methods The Cochrane Library, EMBASE, and PubMed databases were searched, and study methodological quality was assessed using the Newcastle–Ottawa quality assessment scale. To determine the association of the SII with survival outcomes, pooled hazard ratios (HRs) as well as the associated 95% confidence intervals (CIs) were used. To assess the associations of the SII with clinicopathological features, the odds ratios (ORs) and corresponding 95% CIs were considered. Begg’s funnel plot and Egger’s linear regression test were used to assess publication bias. Results A total of 12 studies that together enrolled 4369 patients with HNC were analyzed. In the pooled results, a high pretreatment SII was correlated with poorer overall survival (HR = 2.09, 95% CI = 1.62–2.70, p < 0.001), disease-free survival (HR = 2.79, 95% CI = 1.99−3.89, p < 0.001), and progression-free survival (HR = 1.80, 95% CI = 1.30−2.48, p < 0.001). A stratified analysis indicated that SII for overall survival was applicable regardless of tumor site, treatment modality, overall stage, sample size, SII cutoff, and method for determining the SII cutoff. Furthermore, a high SII was correlated with a more advanced T classification (OR = 1.14, 95% CI = 1.09–1.18, p < 0.001) and nodal metastasis (OR = 1.55, 95% CI = 1.18–2.05, p = 0.002) in patients with HNC. Conclusions An elevated pretreatment SII predicts more advanced tumor and nodal status and poorer survival outcomes in cases of HNC. Because the measurement of SII is convenient and its use is cost-effective, we suggest that it can be applied by clinicians in the management of HNC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".